EBooks/Machine-Learning-For-Finance/4.3 Bayesian Deep Learning.ipynb

{
  "cells": [
    {
      "metadata": {
        "_cell_guid": "2dfac7cb-0962-444e-b916-f112b65375d1",
        "_uuid": "7ec17ee3901a277110bf6c747d0f6490c4be16cf",
        "collapsed": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "import numpy as np",
      "execution_count": 1,
      "outputs": []
    },
    {
      "metadata": {
        "_cell_guid": "ebe0a366-2ee0-43c8-8519-fe3f959332b9",
        "_uuid": "34ed4cdbcd40f56cb07e78632eddf57fb0688178",
        "collapsed": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "X = np.random.rand(20,1) * 10 -5",
      "execution_count": 2,
      "outputs": []
    },
    {
      "metadata": {
        "_cell_guid": "2610c9f9-3491-40d9-af21-e78a33f44961",
        "_uuid": "c8447fd5b1f02d86101d8b3ba5abd06e6d5fce7d",
        "collapsed": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "y = np.sin(X)",
      "execution_count": 3,
      "outputs": []
    },
    {
      "metadata": {
        "_cell_guid": "7f559fff-9974-4f7e-9b63-2433b528ea10",
        "_uuid": "2dc83cf32545908311437b8271ee423b806792a8",
        "trusted": true
      },
      "cell_type": "code",
      "source": "X.shape,y.shape",
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 4,
          "data": {
            "text/plain": "((20, 1), (20, 1))"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "_cell_guid": "f12cd4f9-dc52-4b4b-a945-14548ce9187c",
        "_uuid": "7254087083dd9048c3f04aad3f107b41e9a7faf0",
        "trusted": true
      },
      "cell_type": "code",
      "source": "from keras.models import Sequential\nfrom keras.layers import Dense, Dropout, Activation",
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": "/opt/conda/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n  from ._conv import register_converters as _register_converters\nUsing TensorFlow backend.\n",
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "collapsed": true,
        "_uuid": "cc52b224c5bed33e36d3c68e8f20cdc1c3eb5592",
        "_cell_guid": "5fff833c-ae26-4657-a5c7-b7d6e0073019",
        "trusted": true
      },
      "cell_type": "code",
      "source": "import keras.backend as K\nK.clear_session()\nK.set_learning_phase(1)",
      "execution_count": 6,
      "outputs": []
    },
    {
      "metadata": {
        "_cell_guid": "632af5bd-e3cb-42d4-ad80-d44f2f90980d",
        "_uuid": "5bf45c957274e4c61a3851edbf2d605e7e5aa892",
        "collapsed": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "model = Sequential()\nmodel.add(Dense(1,input_dim = 1))\n\n\nmodel.add(Dropout(0.1))\nmodel.add(Dense(20))\nmodel.add(Activation('relu'))\nmodel.add(Dropout(0.1))\nmodel.add(Dense(20))\nmodel.add(Activation('relu'))\nmodel.add(Dropout(0.1))\nmodel.add(Dense(20))\nmodel.add(Activation('sigmoid'))\nmodel.add(Dense(1))",
      "execution_count": 7,
      "outputs": []
    },
    {
      "metadata": {
        "_cell_guid": "4434fe31-cc0d-4860-a39f-3f18dabca2b4",
        "_uuid": "83742f9c8c5eadb1dd95816e0d3832c881e2962b",
        "collapsed": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "from keras.optimizers import SGD",
      "execution_count": 8,
      "outputs": []
    },
    {
      "metadata": {
        "_cell_guid": "0aba7362-0579-48db-a183-7ef62ee6cd9e",
        "_uuid": "ca4b7bc90fc7525c926f7ce1749f26f3d80f4e3b",
        "collapsed": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "model.compile(loss='mse',optimizer=SGD(lr=0.01))",
      "execution_count": 9,
      "outputs": []
    },
    {
      "metadata": {
        "_cell_guid": "259557f5-5e52-4509-8371-d7444f411c02",
        "_uuid": "4d803efae176188ff8344fa4f3f71c70838ede3d",
        "trusted": true
      },
      "cell_type": "code",
      "source": "model.fit(X,y,epochs=10000,batch_size=10,verbose=0)",
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 10,
          "data": {
            "text/plain": "<keras.callbacks.History at 0x7fef76004128>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "_cell_guid": "6781a450-3211-43e4-a616-2112383fdc37",
        "_uuid": "961a4e1107a84d448ed9f3f8f78a5f91cac1dc9a",
        "collapsed": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "X_test = np.arange(-10,10,0.1)",
      "execution_count": 11,
      "outputs": []
    },
    {
      "metadata": {
        "_cell_guid": "6926d76f-a4e4-4363-addb-299e6b023a2a",
        "_uuid": "57881eedf98ab6e3ba2abc0022ad7633b8a38772",
        "collapsed": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "X_test = np.expand_dims(X_test,-1)",
      "execution_count": 12,
      "outputs": []
    },
    {
      "metadata": {
        "_cell_guid": "a06889a7-a509-4683-af43-93ef384a6749",
        "_uuid": "8d5be14aaa1769419f156c2228eb80a38eab0780",
        "collapsed": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "\nprobs = []\nfor i in range(100):\n    out = model.predict(X_test)\n    probs.append(out)",
      "execution_count": 13,
      "outputs": []
    },
    {
      "metadata": {
        "_cell_guid": "43c9f90b-201f-4b4b-a734-aaad83646a7c",
        "_uuid": "7821632f9c3557ecada1cc28a3cd8ffd6809a934",
        "collapsed": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "p = np.array(probs)",
      "execution_count": 14,
      "outputs": []
    },
    {
      "metadata": {
        "_cell_guid": "c6170ffd-ac3d-4788-bf88-18f482f35003",
        "_uuid": "9e5b526cf7529e3210f89ad4ad8e0368c7ac88b0",
        "trusted": true
      },
      "cell_type": "code",
      "source": "p.shape",
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 15,
          "data": {
            "text/plain": "(100, 200, 1)"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "_cell_guid": "5b391081-e84c-4dd9-bd57-e2260d31ab5d",
        "_uuid": "b32b71abcee294d1d084fb875683b524d6a2ec78",
        "collapsed": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "mean = np.mean(p,axis=0)\nstd = np.std(p,axis=0)\n",
      "execution_count": 16,
      "outputs": []
    },
    {
      "metadata": {
        "_cell_guid": "3a99ac3c-6f6e-406f-bd79-959357be1578",
        "_uuid": "0d59b2fe5816f5a4e2a92a45292238adea07a71b",
        "trusted": true
      },
      "cell_type": "code",
      "source": "import matplotlib.pyplot as plt\nplt.figure(figsize=(10,7))\nplt.plot(X_test,mean,c='blue')\n\nlower_bound = mean - std * 0.5\nupper_bound =  mean + std * 0.5\nplt.fill_between(X_test.flatten(),upper_bound.flatten(),lower_bound.flatten(),alpha=0.25, facecolor='blue')\n\nlower_bound = mean - std\nupper_bound =  mean + std\nplt.fill_between(X_test.flatten(),upper_bound.flatten(),lower_bound.flatten(),alpha=0.25, facecolor='blue')\n\nlower_bound = mean - std * 2\nupper_bound =  mean + std * 2\nplt.fill_between(X_test.flatten(),upper_bound.flatten(),lower_bound.flatten(),alpha=0.25, facecolor='blue')\n\nplt.scatter(X,y,c='black')",
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 17,
          "data": {
            "text/plain": "<matplotlib.collections.PathCollection at 0x7fef6c7369b0>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<matplotlib.figure.Figure at 0x7fef6c759ac8>",
            "image/png": 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73L9aMz6+/gImn2eqEqBb/LvvFu/mFARBEIRqqUWk6i4AvwvgAU3T3vn463ANtltTFhaA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          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "_cell_guid": "a1fe0767-316c-4815-8ef8-65a6a80de559",
        "_uuid": "e538c27b30611076d2b29879ad339b1a93c5b72c",
        "collapsed": true,
        "trusted": false
      },
      "cell_type": "code",
      "source": "",
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "language_info": {
      "name": "python",
      "version": "3.6.4",
      "mimetype": "text/x-python",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 1
}